About The Team

The Connected Devices team at Life360 owns end-to-end device software readiness across our portfolio - firmware, app, and cloud engineering working as one team to ship the trackers and wearables that keep families connected to the people, pets, and things they care about most. We're not just a firmware team: we own the full device software stack, from the hardware modules to the cloud, across the whole lifecycle - architecture and hardware bring-up through mass production and post-launch refinement.

We're moving toward a more intelligent hardware ecosystem, building Life360's first on-device intelligence - processing complex sensor data on the device itself, in real time, within tight power and memory budgets. We're an AI-Native engineering team: AI isn't just a tool we use, it's how we work - across specifications, code, test, review, data analysis and triage. Today that means firmware running across our Tile and Pet GPS tracker lines - devices shipping in the hundreds of thousands of units, each streaming continuous multi-sensor telemetry - which is the scale this on-device ML platform should be built to handle.

This role reports to the Engineering Manager, Connected Devices, and works day-to-day alongside our firmware, app, and cloud engineers, data science, hardware, operations, and data teams.
About the Job

We're looking for a Staff Firmware Engineer to build and own Life360's on-device ML platform - the reusable framework that lets any device in our portfolio sample sensor data, run inference on the edge, and act on it without draining the battery or blowing the memory budget.

This is a hybrid role by design, and both halves are non-negotiable. You are a firmware engineer first - deeply fluent in embedded systems and on-device software, from RTOS internals and driver bring-up to power management and debugging on real hardware. You are an Edge ML specialist second - you know how to get a model running, quantized and optimized, on a Cortex-M-class part with kilobytes to spare, whether it comes from Data Science or you build and train it yourself with modern, AI-assisted tooling. The value of this role is that you own the whole path, from the sensor register to the inference result, without handing off the hard parts.

"Staff" here means you set the technical direction for on-device ML and you ship it - you architect the platform and write the code that proves it works. ML demand will ebb and flow; when it's light, you pick up regular firmware work alongside the team. This is a full firmware seat, not an ML-only one - the ML specialization is what you bring on top of being a strong, contributing firmware engineer.

This is a foundational role. We're shipping our first on-device ML feature now, but there's no reusable platform behind it yet - you'll turn that first feature into the foundation the rest of the portfolio builds on, with a lot of room to grow from there. In year one, success looks like owning on-device ML end to end as your domain - you architect the inference platform, ship it to the fleet, and root-cause its production issues from device logs yourself. You're also a credible firmware engineer across the stack - connectivity, OTA, power, drivers, telemetry - not needing anyone else to own the embedded side. And you've turned that platform into member-facing value: 2-3 on-device ML features shipped to the Pet GPS fleet, learning from real device behavior.

For candidates based in the US, the salary range for this position is $143,000 to $261,500 USD. For candidates based out of Canada, the salary range for this position is $207,000 to $242,500 CAD. Note: Please be aware that the job title for positions in Canada will be "Developer" in lieu of "Engineer." We take into consideration an individual's background and experience in determining final salary; therefore, base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. The compensation package includes a wide range of medical, dental, vision, financial, and other benefits, as well as equity.
What You'll Do
Build and Own the On-Device ML Platform

• Design and build the reusable on-device inference framework any Life360 device can adopt - the runtime, the model integration path, and the sampling and preprocessing pipeline - and make the platform-level calls (runtime and model format, memory/flash budgeting, OTA model updates).
• Own it end to end: you architect it, implement it, and debug it when it misbehaves on a device in the field.

Do the Core Firmware Engineering

• Integrate inference into resource-constrained RTOS firmware (Zephyr / FreeRTOS) without compromising stability, scheduling, or power, and own the low-level plumbing - drivers, DMA data paths, SPI/I²C, and the middleware that feeds the pipeline.
• Debug on real hardware (oscilloscope, logic analyzer, JTAG) and drive cross-layer issues to root cause across the firmware/hardware boundary.
• Carry regular firmware work when ML demand is light - a full share of features, bug fixes, field issues, and on-call/triage.

Develop and Ship Models on Device

• Get models running within firmware constraints - from Data Science or built and trained yourself, increasingly with AI-assisted tooling - handling quantization, operator support, and the latency/memory tradeoffs.
• Own the whole loop, from training output to validated on-device behavior. You don't take a model over the wall and hope.

Optimize for Extreme Constraints

• Squeeze inference into tight power, memory, and latency envelopes, and own the tradeoffs between model accuracy, power draw, and footprint - validated in the real world, not just on the bench.

Set Direction, Work AI-Natively, and Level Up the Team

• Drive alignment across firmware, app/cloud, data science, hardware, and ops on how on-device intelligence gets built here, and raise the team's embedded-ML fluency through code review, design docs, and pairing.
• Use AI tooling as a genuine development partner across firmware and ML, and help define what AI-native looks like for embedded work.

What We're Looking For
Foundation

• 10+ years of firmware engineering, taking complex consumer hardware from prototype through mass production, with a demonstrated track record of shipping at scale.
• Bachelor's degree in Electrical Engineering, Computer Science, or a related field.

Firmware Engineering - the core bar

• Deep C/C++ for embedded systems and real fluency in RTOS internals (Zephyr, FreeRTOS, or equivalent).
• Strong low-level hardware skills - SPI/I²C/UART, DMA, interrupts, driver development from the datasheet up - plus hands-on debugging (scope, logic analyzer, JTAG) and power/memory discipline.

Edge ML

• Demonstrated experience deploying ML models on microcontroller-class hardware in a shipping product - not a course project or one-off PoC.
• Hands-on with embedded inference frameworks (TFLite Micro, CMSIS-NN, ExecuTorch, or equivalent) and model optimization (quantization, pruning); able to develop and train models yourself, not only deploy someone else's.
• Solid grounding in the sensor data and signal-processing pipelines that feed a model (IMU and similar).

AI-Native and Communication

• You use AI coding tools (Claude Code or equivalent) as a genuine development partner and review their output as critically as any PR.
• Strong written communication and a habit of documenting decisions and working across firmware, hardware, and data science.

Nice to Have

• Security and compliance for connected devices - secure boot, key provisioning, signed and rollback-safe OTA, and RF/regulatory certification (FCC/CE).
• Hands-on experience with cellular, BLE, GPS/GNSS, or audio subsystems on battery-powered wearables or trackers.
• New-board bring-up - powering up unproven hardware, verifying power rails and peripherals, and getting first firmware running on a fresh board.
• Hardware schematic evaluation - reading and reviewing schematics, partnering with EE on design reviews, and catching issues before a board spin.
• Factory and manufacturing support - manufacturing test development, production-line bring-up and debug, and supporting yield and quality at the contract manufacturer.

AI-Native Expectations

• Daily use - you use AI tools (Claude Code, Cursor, or equivalent) every day for real firmware and ML work: drafting and refactoring code, debugging on-device failures, writing specs and design docs, and building or validating models. This isn't autocomplete; it's a standing part of how you work.
• Judgment and ownership - you review every line of AI-generated code and every AI-assisted model output as critically as you would a human PR before it ships to a device in the field. The AI writes it; you're accountable for it.
• Velocity - you're expected to use AI to compress timelines that used to take weeks, from specifications to working prototype to validated on-device behavior, and to be able to explain your approach when you don't.
• Team leadership - you share what you learn (patterns that work, guardrails you've added, failure modes you've hit) through code review, design docs, and pairing, and you help raise the team's AI fluency, not just your own.
• Continuous learning - you stay current on embedded-AI tooling and techniques and bring recommendations back to the team rather than waiting for them to arrive.

Our Benefits

• Competitive pay and benefits.
• Medical, dental, vision, life and disability insurance plans (100% paid for US employees). We offer supplemental plans for medical and dental for Canadian employees.
• 401(k) plan with company matching program in the US and RRSP with DPSP plan for Canadian employees.
• Employee Assistance Program (EAP) for mental wellness.
• Flexible PTO and 12 company-wide days off throughout the year.
• Winter and Summer Weeklong Synchronized Company Shutdowns
• Learning & Development programs.
• Equipment, tools, and reimbursement support for a productive remote environment.
• Free Life360 Platinum Membership for your preferred circle.
• Free Tile Products